Electrical impedance tomography (EIT) provides functional images of anelectrical conductivity distribution inside the human body. Since the 1980s,many potential clinical applications have arisen using inexpensive portable EITdevices. EIT acquires multiple trans-impedance measurements across the bodyfrom an array of surface electrodes around a chosen imaging slice. Theconductivity image reconstruction from the measured data is a fundamentallyill-posed inverse problem notoriously vulnerable to measurement noise andartifacts. Most available methods invert the ill-conditioned sensitivity orJacobian matrix using a regularized least-squares data-fitting technique. Theirperformances rely on the regularization parameter, which controls the trade-offbetween fidelity and robustness. For clinical applications of EIT, it would bedesirable to develop a method achieving consistent performance over variousuncertain data, regardless of the choice of the regularization parameter. Basedon the analysis of the structure of the Jacobian matrix, we propose afidelity-embedded regularization (FER) method and a motion artifact removalfilter. Incorporating the Jacobian matrix in the regularization process, thenew FER method with the motion artifact removal filter offers stablereconstructions of high-fidelity images from noisy data by taking a very largeregularization parameter value. The proposed method showed practical merits inexperimental studies of chest EIT imaging.
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